Непараметрические Оценки Эффективности Российских Банков [Nonparametric estimates of Russian banks efficiency]
Bibliographic record
Abstract
Non-parametric estimates of technical efficiency of Russian banks are considered for each quarter in the period of 2002–2006. Two types of DEA estimates CCR (Charnes, Cooper, Rhodes, 1978) and BCC (Banker, Charnes, Cooper, 1984), are compared with parametric SFA estimates. Semiparametric bootstrap (Simar, Wilson, 2007) is used to study statistical properties of DEA estimates. Spearman rank correlation between CCR and BCA estimates vary from 0.72 to 0.89 and between DEA and SFA from 0.56 to 0.91, hence estimates are robust. Foreign banks are more efficient than domestic banks in all quarters with the only exception of 2004Q2, which could be explained by so-called “crisis of confidence” (bank crisis in Russia in that period). Since 2006 Moscow banks are less efficient than the regional banks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".